Postmortem Program Analysis from a Conventional Program Analysis Method to an AI-assisted Approach

Abstract: Despite the best efforts of developers, software inevitably contains flaws that may be leveraged as security vulnerabilities. Modern operating systems integrate various security mechanisms to prevent software faults from being exploited. To bypass these defenses and hijack program execution, an attacker needs to constantly mutate an exploit and make many attempts. While in their attempts, the exploit triggers a security vulnerability and makes the running process abnormally terminate.

After a program has crashed and abnormally terminated, it typically leaves behind a snapshot of its crashing state in the form of a core dump. While a core dump carries a large amount of information, which has long been used for software debugging, it barely serves as informative debugging aids in locating software faults, particularly memory corruption vulnerabilities. As such, previous research mainly seeks fully reproducible execution tracing to identify software vulnerabilities in crashes. However, such techniques are usually impractical for complex programs. Even for simple programs, the overhead of fully reproducible tracing may only be acceptable at the time of in-house testing.

In this talk, I will discuss how we tackle this issue by bridging program analysis with artificial intelligence (AI). More specifically, I will first talk about the history of postmortem program analysis, characterizing and disclosing their limitations. Second, I will introduce how we design a new reverse-execution approach for postmortem program analysis. Third, I will discuss how we integrate AI into our reverse-execution method to escalate its analysis efficiency and accuracy. Last but not least, as part of this talk, I will demonstrate the effectiveness of this AI-assisted postmortem program analysis framework by using massive amounts of real-world programs.

Bio: Dr. Xinyu Xing is an Assistant Professor at Pennsylvania State University. His research interests include exploring, designing and developing new program analysis and AI techniques to automate vulnerability discovery, failure reproduction, vulnerability diagnosis (and triage), exploit and security patch generation. His past research has been featured by many mainstream media and received the best paper awards from ACM CCS and ACSAC. Going beyond academic research, he also actively participates and hosts many world-class cybersecurity competitions (such as HITB and XCTF). As the founder of JD-OMEGA, his team has been selected for DEFCON/GeekPwn AI challenge grand final at Las Vegas. Currently, his research is mainly supported by NSF, ONR, NSA and industry partners.

Abstract:
Quantum Machine Learning (QML) holds significant promise for solving computational challenges across diverse domains. However, its practical deployment is constrained by the limitations of noisy intermediate-scale quantum (NISQ) devices, including noise, limited scalability, and trainability issues in variational quantum circuits (VQCs). We introduce the multi-chip ensemble VQC framework, which partitions high-dimensional computations across smaller quantum chips to enhance scalability, trainability, and noise resilience. We show that this approach mitigates barren plateaus, reduces quantum error bias and variance, and maintains robust generalization through controlled entanglement. Designed to align with current and emerging quantum hardware, the framework demonstrates strong potential for enabling scalable QML on near-term devices, as validated by experiments on standard benchmark datasets (MNIST, FashionMNIST, CIFAR-10).

IACS Student Seminar Speaker:
Junghoon Park, Seoul National University
BA in Economics, Seoul National University, Korea
PhD Candidate for Interdisciplinary Programme in Artificial Intelligence at Seoul National University
Visiting Researcher at Brookhaven National Laboratory


Current Research Interests
Quantum Machine Learning


Recent Papers
Park, J., Cha, J., Chen, S. Y.-C., Yoo, S., & Tseng, H.-H. (2025). Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles. In Review at ICML.
Park, J., Kim, K., & Cha, J. (2025). How to Assess AI Ethics: Suggestions for Ethical Rating Agencies. In Review at IJCAI.
Park, J., Cha, J., Chen, S. Y.-C., Yoo, S., & Tseng, H.-H. (2024, 15-20 Sept.). Over the Quantum Rainbow: Explaining Hybrid Quantum Reinforcement Learning. 2024 IEEE International Conference on Quantum Computing and Engineering (QCE).
Park, J., Lee, E., Cho, G., Hwang, H., Kim, B.-G., Kim, G., Joo, Y. Y., & Cha, J. (2024). Gene-Environment Pathways to Cognitive Intelligence and Psychotic-Like Experiences in Children. eLife, 12, RP88117. DOI:10.7554/eLife.88117

This seminar will be held in person (food provided!) in the IACS Seminar Room, and online (zoom link below!)
https://stonybrook.zoom.us/j/96548538719?pwd=jBmI43H68q2UkdcRRjVbTkgrC6F942.1
Meeting ID: 965 4853 8719
Passcode: 493290

Join the Collaborative for the Earth, the Department of Political Science, and the AI Innovation Institute as we welcome Lav Varshney, Ph.D., Della Pietra Infinity Professor and Inaugural Director of the AI Innovation Institute.

The Physical Economy of Intelligence: Marketcraft, Data Centers, and Climate

Artificial intelligence is often described as immaterial, yet it is a rapidly growing physical industry built from memory chips, servers, concrete, land, electricity, water, and capital. This talk develops a marketcraft perspective on AI and climate, treating memory, compute, and intelligence as interdependent goods shaped by supply constraints, demand shocks, long investment cycles, environmental externalities, and the design of markets and institutions. This framing helps explain why sustainable AI cannot be achieved through more efficient algorithms alone: it also requires better lifecycle accounting, procurement standards, flexible computing, and contracts that shift computational activity toward environmentally favorable times and places. The talk will connect these system-level ideas to work on generative design and experimental validation of low-carbon concrete for data-center construction, as well as AI-enabled optimization of wastewater operations that have each reduced energy/carbon by 30 percent or more. Together, these examples show how the co-design of algorithms, physical infrastructure, markets, and public policy can reduce AI's environmental footprint while using AI to accelerate climate solutions.

Date & Time: Wednesday, September 23, 2026 | 12:30 PM - 1:50 PM

Location: Laufer Center

Registration is compulsory

For any questions and accessibility requests or accommodations, please contact Jennifer Gilday at c4e@stonybrook.edu or call 631-632-4625.

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk on AI research happening at BNL, followed by an open mixer. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

We meet every other Tuesday at noon in CDSD's Training Room (building 725, room 2-124) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

In addition to our speaker, we will have a number of CDS staff in attendance with expertise in AI methods and applications including image analysis, foundation models development, and inverse problem solving.

AI-Driven Physics-Informed Phase Retrieval from a Single X-ray

Abstract: X-ray phase-contrast imaging enables the visualization of weakly absorbing or low-contrast structures and plays an important role in materials, biological, and energy research. Conventional X-ray holography and phase-retrieval techniques typically require multiple intensity measurements acquired at different propagation distances to recover phase information, increasing acquisition time, radiation dose, and experimental complexity. In this work, we present an AI-driven, physics-informed approach for phase retrieval using only a single X-ray intensity measurement. The method adapted a generative neural network as an inverse reconstruction engine, with physical models of X-ray wave propagation embedded directly into the optimization process. This allows phase and absorption information to be recovered from a single hologram without relying on paired, unpaired, or simulated training datasets. By combining physical constraints with self-supervised AI reconstruction, the approach achieves stable and quantitative results across a wide range of imaging conditions. The results demonstrate how physics-informed AI can reduce experimental requirements and enable data-efficient, automated phase retrieval for next-generation X-ray imaging workflows.

Biography: Xiaogang Yang is a computational scientist in the Data Analysis & Workflow Integration group at NSLS-II, focusing on AI development for X-ray imaging, data analysis, and automated workflows. He earned his PhD from Delft University of Technology, completed his postdoctoral research at Argonne National Laboratory, and previously held a tenured position at PETRA III (DESY).

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1604383624?pwd=ffQ5cUPNxTI7nzClKQO6cnsNbhF9Vf.1

Meeting ID: 160 438 3624
Passcode: 558449

Please Note: Due to a funding shortfall, we are for the time being no longer able to provide pizza and sodas for these events. We will have coffee though, and all are of course welcome to bring their lunch.

The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors. Each seminar will consist of multiple short talks (around 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Everyone else is welcome to attend. Fill in https://forms.gle/q6UG9ygauLp2a8Po8 to subscribe to our mailing list for further announcement.
Face Editing with Machine Learning presented by Zhixin Shu

ABSTRACT: The face is the most informative feature of humans and has been a long-standing research topic in Computer Vision and Graphics. Images of faces are also ubiquitous in photography and social media, and people have devoted significant resources to capturing and editing face images. Face editing can be broadly viewed as the encoding, manipulation and the decoding of some representations for face images. The challenges are that we want to manipulate an image in a controllable way and generate results that are both desirable and as realistic as possible. This thesis explores different Machine Learning-based face-editing approaches. I discuss the role of machine learning for achieving desirable edits by learning both the physical aspects as well as the statistical manifold of human faces. In my work for eye-editing, I discuss the importance of understanding multiple physical elements of a face image, such as shape, illumination, pose, etc. In a deep-learning-based approach, I introduce image formation domain knowledge to the construction and training of a neural network. This network provides transparent access to the disentangled representations of the aforementioned physical properties. With this network, we can achieve various face editing tasks in forms of representation manipulation. After that, I introduce Deforming Autoencoders, a network that learns to disentangle shape and appearance in an unsupervised manner. This disentanglement is beneficial for the learning of some other factors of variations, such as illumination and facial expression. In an extension of Deforming Autoencoders, we incorporate non-rigid structure-from-motion to learn a 3D morphable model for faces that only requires an image set for training. At last, I describe an image-to-image network for 3D face reconstruction, which also utilizes structure-from-motion in deep learning. With real face images in training, this network not only reconstructs 3D faces more accurately than prior art but also has better generalization ability in real-life testing cases.

https://stonybrook.zoom.us/j/94414957054?pwd=V1JMc2EwSnVGMFdaUlNobE9DSHU4dz09#success
ID: 94414957054
Password: 094758

Speaker: Heather J. Lynch


Bio:  Dr. Heather J. Lynch is an Associate Professor of Ecology & Evolution at Stony Brook University. Prior to Stony Brook, Dr. Lynch was an Adjunct Professor of Applied Math and Statistics at UC Santa Cruz and a Research Scientist in the Biology Department at the University Maryland. Dr. Lynch received her A.B. in Physics from Princeton University in 2000, an A.M. in Physics from Harvard University in 2004, and a Ph.D. in Organismal and Evolutionary Biology from Harvard University in 2006. Dr. Lynch's research is focused on spatial population dynamics of Antarctic penguins, with a particular focus on statistical and mathematical models to integrate patchy time series with remote sensing imagery. These data will allow Dr. Lynch and colleagues to develop mathematical models to explore how coloniality constrains the colonization and extinction of individual habitat patches and, ultimately, the metapopulation dynamics of colonial seabirds.   

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

Learning Generalizable Program and Architecture Representations for Performance Modeling

Abstract: Performance modeling is an essential tool in many areas of computer science and engineering. However, existing performance modeling approaches have limitations, such as high computational cost, narrow flexibility, or restricted accuracy/generality. To address these limitations, this talk introduces PerfVec, a novel deep learning-based performance modeling framework that learns high-dimensional and independent/orthogonal program and microarchitecture representations. Once learned, a program representation can be used to predict its performance on any microarchitecture, and likewise, a microarchitecture representation can be applied in the performance prediction of any program. Additionally, PerfVec yields a foundation model that captures the performance essence of instructions, which can be directly used by developers in numerous performance modeling-related tasks without incurring its training cost. The evaluation demonstrates that PerfVec is more general and efficient than previous approaches. This talk will also introduce how PerfVec's design principles can benefit broader research areas.

Biography: Lingda Li is a computer scientist at Brookhaven National Laboratory. He is generally interested in computer architecture and programming model research, with focus on simulation/modeling, memory systems, and machine learning. Before joining BNL, he worked at the Department of Computer Science of Rutgers University as a postdoc to carry out GPGPU research. He obtained a PhD in computer architecture from the Microprocessor Research and Development Center at Peking University.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1605837856?pwd=kYqJs4bVBt4E0cMCWR6GXH3wxzOoiw.1

Meeting ID: 160 583 7856
Passcode: 161580